Distinguishing noise from chaos

Chaotic systems share with stochastic processes several properties that make them almost undistinguishable. In this communication we introduce a representation space, to be called the complexity-entropy causality plane. Its horizontal and vertical axis are suitable functionals of the pertinent proba...

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Detalles Bibliográficos
Autores principales: Rosso, Osvaldo A., Larrondo, Hilda Ángela, Martín, María Teresa, Plastino, Ángel Luis, Fuentes, Miguel A.
Formato: Articulo
Lenguaje:Inglés
Publicado: 2007
Materias:
Física
Ciencias Exactas
Chaotic systems
Entropy (information theory)
Representation space
Acceso en línea:http://sedici.unlp.edu.ar/handle/10915/126151
https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.99.154102
Aporte de:
SEDICI (UNLP) de Universidad Nacional de La Plata
id I19-R120-10915-126151
record_format dspace
institution Universidad Nacional de La Plata
institution_str I-19
repository_str R-120
collection SEDICI (UNLP)
language Inglés
topic Física
Ciencias Exactas
Chaotic systems
Entropy (information theory)
Representation space
spellingShingle Física
Ciencias Exactas
Chaotic systems
Entropy (information theory)
Representation space
Rosso, Osvaldo A.
Larrondo, Hilda Ángela
Martín, María Teresa
Plastino, Ángel Luis
Fuentes, Miguel A.
Distinguishing noise from chaos
topic_facet Física
Ciencias Exactas
Chaotic systems
Entropy (information theory)
Representation space
description Chaotic systems share with stochastic processes several properties that make them almost undistinguishable. In this communication we introduce a representation space, to be called the complexity-entropy causality plane. Its horizontal and vertical axis are suitable functionals of the pertinent probability distribution, namely, the entropy of the system and an appropriate statistical complexity measure, respectively. These two functionals are evaluated using the Bandt-Pompe recipe to assign a probability distribution function to the time series generated by the system. Several well-known model-generated time series, usually regarded as being of either stochastic or chaotic nature, are analyzed so as to illustrate the approach. The main achievement of this communication is the possibility of clearly distinguishing between them in our representation space, something that is rather difficult otherwise.
format Articulo
Articulo
author Rosso, Osvaldo A.
Larrondo, Hilda Ángela
Martín, María Teresa
Plastino, Ángel Luis
Fuentes, Miguel A.
author_facet Rosso, Osvaldo A.
Larrondo, Hilda Ángela
Martín, María Teresa
Plastino, Ángel Luis
Fuentes, Miguel A.
author_sort Rosso, Osvaldo A.
title Distinguishing noise from chaos
title_short Distinguishing noise from chaos
title_full Distinguishing noise from chaos
title_fullStr Distinguishing noise from chaos
title_full_unstemmed Distinguishing noise from chaos
title_sort distinguishing noise from chaos
publishDate 2007
url http://sedici.unlp.edu.ar/handle/10915/126151
https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.99.154102
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AT larrondohildaangela distinguishingnoisefromchaos
AT martinmariateresa distinguishingnoisefromchaos
AT plastinoangelluis distinguishingnoisefromchaos
AT fuentesmiguela distinguishingnoisefromchaos
bdutipo_str Repositorios
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